Multi-label Ranking: Mining Multi-label and Label Ranking Data

Multi-label ranking (MLR) is the problem of predicting and ranking multiple labels for a single instance. The predicted labels are known as the instance's labelset. MLR can be typically reduced to two sub-problems: The first is multi-label classification, where the task is to bipartite the data into relevant labels (the labelset) and irrelevant labels. The second is label ranking classification, where the task is to rank labels for each instance. A label ranking may contain ties; in the extreme case relevant labels hold a tie on first place, and irrelevant labels hold a tie on second place, thus turning the label ranking classification into a multi-label one.